M. BENAZI Makhlouf

MCA

Directory of teachers

Department

Informatics Department

Research Interests

I A Data Mining Social Networks Clustering Bio-inspired Optimization

Contact Info

University of M'Sila, Algeria

Email : makhlouf.benazi@univ-msila.dz

On the Web:

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Recent Publications

2024-12-10

Simplifying the DBSCAN Algorithm with a Single-Parameter Approach.

This paper presents an enhanced version of the DBSCAN algorithm, termed myDBSCAN, which
simplifies the clustering process by relying on a single parameter, `eps`, instead of the traditional two
parameters used in the original DBSCAN. The study evaluates myDBSCAN's performance across
various synthetic datasets with different shapes and levels of noise. Empirical results demonstrate
myDBSCAN’s performance comparably to the original DBSCAN algorithm, successfully identifying
clusters with similar accuracy. The simplicity of using only one parameter makes myDBSCAN more
accessible and easier to implement. The results highlighted myDBSCAN's effectiveness in clustering
tasks, offering a practical alternative to traditional DBSCAN, particularly in scenarios where ease of
parameter tuning is crucial. Future research will explore further optimizations and applications of
myDBSCAN to a broader range of datasets.
Citation

M. BENAZI Makhlouf, (2024-12-10), "Simplifying the DBSCAN Algorithm with a Single-Parameter Approach.", [international] The Sixth International Symposium on Informatics and Its Applications (ISIA) , University of M’sila

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2024-07-03

A robust two‑step algorithm for community detection based on node similarity

The rapid development of the internet and social network platforms has given rise to a new field of research, social network analysis. This field of research has many fundamental problems, one of which is community detection. The objective of this research is to understand hidden connections among individuals. However, uncovering these connections are still challenging, despite the existence of several methods. In this paper, we propose a new algorithm called MCCD (Modified Cosine for Community Detection) for community detection in social networks based on node similarity. Our algorithm consists of two steps. In the first step, we use a novel cosine similarity formula to identify initial communities. In the second step, we merge these communities based on a new similarity measure. MCCD can be used in two different ways. The first way uses K as an input to identify the exact communities. The second way does not require K and aims to provide the best partitioning by maximizing modularity. Our algorithm has been tested on a variety of artificial and real-world networks, and the experimental results demonstrate its superiority over existing methods in detecting communities.
Citation

M. BENAZI Makhlouf, (2024-07-03), "A robust two‑step algorithm for community detection based on node similarity", [national] The Journal of Supercomputing , Springer Nature

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2024-04-18

An Adaptative Eps Parameter of DBSCAN Algorithm for Identifying Clusters with Heterogeneous Density

Density-Based Spatial Clustering of
Applications with Noise (DBSCAN) is one of the most
important data clustering algorithms. Its importance lies
in the fact that it can recognize clusters of arbitrary
shapes and is not affected by noise in the data. To
identify clusters, DBSCAN needs to specify two
parameters: the parameter Eps, representing the radius
of the circle to identify the neighborhood of each
observation. The second parameter of DBSCAN is
minpts, which represents the minimum size of the
neighborhood for a point to be a seed in a cluster and
not a noise. However, the task of determining the
adequate value of Eps parameter is not easy and
represents a major issue when applying DBSCAN since
the accuracy of this algorithm highly depends on the
values of its parameters. In this paper, we present a new
version of DBSCAN where we need only to specify the
minpts parameter, then we use k-nearest neighbors
(kNN) algorithm to calculate the value of Eps
automatically for every point in the data. This technique
not only reduces the number of parameters by
eliminating Eps which has been very difficult to
determine, but also gives DBSCAN the ability to detect
clusters with heterogeneous density. The experimental
results show that the proposed method is more efficient
and more accurate than the original DBSCAN algorithm.
Citation

M. BENAZI Makhlouf, (2024-04-18), "An Adaptative Eps Parameter of DBSCAN Algorithm for Identifying Clusters with Heterogeneous Density", [national] Computación y Sistemas , https://www.cys.cic.ipn.mx/ojs/index.php/CyS/about

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2022

A complex network community detection algorithm based on random walk and label propagation

The community structure is proving to have a very important role in the understanding of
complex networks, but discovering them remains a very diÕcult problem despite the
existence of several methods. In this article, we propose a novel algorithm for discovering
communities in complex networks based on a modiÒed random walk (RW) and label
propagation algorithm (LPA). First, we calculate the similarity between nodes based on the
new formula of RW. Then, the labels are propagated by the obtained similarity of the Òrst
step using LPA. Finally, the third step will be a new measure to Ònd the optimal partitioning
of communities. Experimental results obtained on several real and synthetic networks
reveal that our algorithm outperforms existing methods in Ònding communities.
Citation

M. BENAZI Makhlouf, (2022), "A complex network community detection algorithm based on random walk and label propagation", [national] Transactions on Emerging Telecommunications Technologies. , Wiley

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